IMPORTS¶

In [1]:
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
import sklearn

from prophet import Prophet

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.max_columns', None)

Third Dataset¶

The last dataset, ETO, contains aggregated and transformed information of the weather stations, the variables are constructed with the pattern (except the variables date and ID_ESTACION) Variable + "Local" + period + type of aggregation.

The data:

• date: Day (Numeric)
• DewPoint: Dew point, temperature to which air must be cooled at constant pressure to reach saturation. DewPoint is also an indirect measure of air humidity (Degrees Kelvin).
• Evapotranspiration: Evapotranspiration of the reference crop. This is a rate that gives the amount of water lost by a reference crop (mm/h).
• FeelsLike: Heat sensation. Apparent temperature resulting from the combination of temperature, humidity and wind speed (Degrees Kelvin).
• GlobalHorizontalIrrandiance: Total amount of solar radiation received on a horizontal surface (W/m2).
• Gust: Maximum wind gust speed recorded during the observation period (m/s).
• MSLP: Barometric pressure (Pa).
• precipAmount: Hourly rainfall volume (mm/h).
• relativeHumidity: Relative humidity (%).
• SnowAmount: Hourly snowfall volume (m/h).
• Temperature: Ambient temperature at 2 metres above the ground (Kelvin degrees).
• uvIndex: Ultraviolet radiation: 0-2 = low / 3-5 = moderate / 6-7 = high / 8-10 = very high / 11-16 = extreme.
• visibility: Horizontal visibility from the weather station, 999 equals unlimited (m).
• windSpeed: Wind speed (m/s).
• ID_ESTACION: Identifier of the weather station.

Aggregations:

• Min: Minimum of the period.
• Avg: Average of the period.
• Max: Maximum of the period.

DATA PREPARATION¶

Import and brief exploration of the dataset

In [2]:
ETO = pd.read_csv('./Datos_Originales/DATOS_ETO.TXT',sep = '|', na_values = 'NA', encoding='UTF-8')
In [3]:
ETO
Out[3]:
date DewpointLocalAfternoonAvg DewpointLocalAfternoonMax DewpointLocalAfternoonMin DewpointLocalDayAvg DewpointLocalDayMax DewpointLocalDayMin DewpointLocalDaytimeAvg DewpointLocalDaytimeMax DewpointLocalDaytimeMin DewpointLocalEveningAvg DewpointLocalEveningMax DewpointLocalEveningMin DewpointLocalMorningAvg DewpointLocalMorningMax DewpointLocalMorningMin DewpointLocalNighttimeAvg DewpointLocalNighttimeMax DewpointLocalNighttimeMin DewpointLocalOvernightAvg DewpointLocalOvernightMax DewpointLocalOvernightMin EvapotranspirationLocalAfternoonAvg EvapotranspirationLocalAfternoonMax EvapotranspirationLocalAfternoonMin EvapotranspirationLocalDayAvg EvapotranspirationLocalDayMax EvapotranspirationLocalDayMin EvapotranspirationLocalDaytimeAvg EvapotranspirationLocalDaytimeMax EvapotranspirationLocalDaytimeMin EvapotranspirationLocalEveningAvg EvapotranspirationLocalEveningMax EvapotranspirationLocalEveningMin EvapotranspirationLocalMorningAvg EvapotranspirationLocalMorningMax EvapotranspirationLocalMorningMin EvapotranspirationLocalNighttimeAvg EvapotranspirationLocalNighttimeMax EvapotranspirationLocalNighttimeMin EvapotranspirationLocalOvernightAvg EvapotranspirationLocalOvernightMax EvapotranspirationLocalOvernightMin FeelsLikeLocalAfternoonAvg FeelsLikeLocalAfternoonMax FeelsLikeLocalAfternoonMin FeelsLikeLocalDayAvg FeelsLikeLocalDayMax FeelsLikeLocalDayMin FeelsLikeLocalDaytimeAvg FeelsLikeLocalDaytimeMax FeelsLikeLocalDaytimeMin FeelsLikeLocalEveningAvg FeelsLikeLocalEveningMax FeelsLikeLocalEveningMin FeelsLikeLocalMorningAvg FeelsLikeLocalMorningMax FeelsLikeLocalMorningMin FeelsLikeLocalNighttimeAvg FeelsLikeLocalNighttimeMax FeelsLikeLocalNighttimeMin FeelsLikeLocalOvernightAvg FeelsLikeLocalOvernightMax FeelsLikeLocalOvernightMin GlobalHorizontalIrradianceLocalAfternoonAvg GlobalHorizontalIrradianceLocalAfternoonMax GlobalHorizontalIrradianceLocalAfternoonMin GlobalHorizontalIrradianceLocalDayAvg GlobalHorizontalIrradianceLocalDayMax GlobalHorizontalIrradianceLocalDayMin GlobalHorizontalIrradianceLocalDaytimeAvg GlobalHorizontalIrradianceLocalDaytimeMax GlobalHorizontalIrradianceLocalDaytimeMin GlobalHorizontalIrradianceLocalEveningAvg GlobalHorizontalIrradianceLocalEveningMax GlobalHorizontalIrradianceLocalEveningMin GlobalHorizontalIrradianceLocalMorningAvg GlobalHorizontalIrradianceLocalMorningMax GlobalHorizontalIrradianceLocalMorningMin GlobalHorizontalIrradianceLocalNighttimeAvg GlobalHorizontalIrradianceLocalNighttimeMax GlobalHorizontalIrradianceLocalNighttimeMin GlobalHorizontalIrradianceLocalOvernightAvg GlobalHorizontalIrradianceLocalOvernightMax GlobalHorizontalIrradianceLocalOvernightMin GustLocalAfternoonAvg GustLocalAfternoonMax GustLocalAfternoonMin GustLocalDayAvg GustLocalDayMax GustLocalDayMin GustLocalDaytimeAvg GustLocalDaytimeMax GustLocalDaytimeMin GustLocalEveningAvg GustLocalEveningMax GustLocalEveningMin GustLocalMorningAvg GustLocalMorningMax GustLocalMorningMin GustLocalNighttimeAvg GustLocalNighttimeMax GustLocalNighttimeMin GustLocalOvernightAvg GustLocalOvernightMax GustLocalOvernightMin MSLPLocalAfternoonAvg MSLPLocalAfternoonMax MSLPLocalAfternoonMin MSLPLocalDayAvg MSLPLocalDayMax MSLPLocalDayMin MSLPLocalDaytimeAvg MSLPLocalDaytimeMax MSLPLocalDaytimeMin MSLPLocalEveningAvg MSLPLocalEveningMax MSLPLocalEveningMin MSLPLocalMorningAvg MSLPLocalMorningMax MSLPLocalMorningMin MSLPLocalNighttimeAvg MSLPLocalNighttimeMax MSLPLocalNighttimeMin MSLPLocalOvernightAvg MSLPLocalOvernightMax MSLPLocalOvernightMin PrecipAmountLocalAfternoonAvg PrecipAmountLocalAfternoonMax PrecipAmountLocalAfternoonMin PrecipAmountLocalDayAvg PrecipAmountLocalDayMax PrecipAmountLocalDayMin PrecipAmountLocalDaytimeAvg PrecipAmountLocalDaytimeMax PrecipAmountLocalDaytimeMin PrecipAmountLocalEveningAvg PrecipAmountLocalEveningMax PrecipAmountLocalEveningMin PrecipAmountLocalMorningAvg PrecipAmountLocalMorningMax PrecipAmountLocalMorningMin PrecipAmountLocalNighttimeAvg PrecipAmountLocalNighttimeMax PrecipAmountLocalNighttimeMin PrecipAmountLocalOvernightAvg PrecipAmountLocalOvernightMax PrecipAmountLocalOvernightMin RelativeHumidityLocalAfternoonAvg RelativeHumidityLocalAfternoonMax RelativeHumidityLocalAfternoonMin RelativeHumidityLocalDayAvg RelativeHumidityLocalDayMax RelativeHumidityLocalDayMin RelativeHumidityLocalDaytimeAvg RelativeHumidityLocalDaytimeMax RelativeHumidityLocalDaytimeMin RelativeHumidityLocalEveningAvg RelativeHumidityLocalEveningMax RelativeHumidityLocalEveningMin RelativeHumidityLocalMorningAvg RelativeHumidityLocalMorningMax RelativeHumidityLocalMorningMin RelativeHumidityLocalNighttimeAvg RelativeHumidityLocalNighttimeMax RelativeHumidityLocalNighttimeMin RelativeHumidityLocalOvernightAvg RelativeHumidityLocalOvernightMax RelativeHumidityLocalOvernightMin SnowAmountLocalAfternoonAvg SnowAmountLocalAfternoonMax SnowAmountLocalAfternoonMin SnowAmountLocalDayAvg SnowAmountLocalDayMax SnowAmountLocalDayMin SnowAmountLocalDaytimeAvg SnowAmountLocalDaytimeMax SnowAmountLocalDaytimeMin SnowAmountLocalEveningAvg SnowAmountLocalEveningMax SnowAmountLocalEveningMin SnowAmountLocalMorningAvg SnowAmountLocalMorningMax SnowAmountLocalMorningMin SnowAmountLocalNighttimeAvg SnowAmountLocalNighttimeMax SnowAmountLocalNighttimeMin SnowAmountLocalOvernightAvg SnowAmountLocalOvernightMax SnowAmountLocalOvernightMin TemperatureLocalAfternoonAvg TemperatureLocalAfternoonMax TemperatureLocalAfternoonMin TemperatureLocalDayAvg TemperatureLocalDayMax TemperatureLocalDayMin TemperatureLocalDaytimeAvg TemperatureLocalDaytimeMax TemperatureLocalDaytimeMin TemperatureLocalEveningAvg TemperatureLocalEveningMax TemperatureLocalEveningMin TemperatureLocalMorningAvg TemperatureLocalMorningMax TemperatureLocalMorningMin TemperatureLocalNighttimeAvg TemperatureLocalNighttimeMax TemperatureLocalNighttimeMin TemperatureLocalOvernightAvg TemperatureLocalOvernightMax TemperatureLocalOvernightMin UVIndexLocalAfternoonAvg UVIndexLocalAfternoonMax UVIndexLocalAfternoonMin UVIndexLocalDayAvg UVIndexLocalDayMax UVIndexLocalDayMin UVIndexLocalDaytimeAvg UVIndexLocalDaytimeMax UVIndexLocalDaytimeMin UVIndexLocalEveningAvg UVIndexLocalEveningMax UVIndexLocalEveningMin UVIndexLocalMorningAvg UVIndexLocalMorningMax UVIndexLocalMorningMin UVIndexLocalNighttimeAvg UVIndexLocalNighttimeMax UVIndexLocalNighttimeMin UVIndexLocalOvernightAvg UVIndexLocalOvernightMax UVIndexLocalOvernightMin VisibilityLocalAfternoonAvg VisibilityLocalAfternoonMax VisibilityLocalAfternoonMin VisibilityLocalDayAvg VisibilityLocalDayMax VisibilityLocalDayMin VisibilityLocalDaytimeAvg VisibilityLocalDaytimeMax VisibilityLocalDaytimeMin VisibilityLocalEveningAvg VisibilityLocalEveningMax VisibilityLocalEveningMin VisibilityLocalMorningAvg VisibilityLocalMorningMax VisibilityLocalMorningMin VisibilityLocalNighttimeAvg VisibilityLocalNighttimeMax VisibilityLocalNighttimeMin VisibilityLocalOvernightAvg VisibilityLocalOvernightMax VisibilityLocalOvernightMin WindSpeedLocalAfternoonAvg WindSpeedLocalAfternoonMax WindSpeedLocalAfternoonMin WindSpeedLocalDayAvg WindSpeedLocalDayMax WindSpeedLocalDayMin WindSpeedLocalDaytimeAvg WindSpeedLocalDaytimeMax WindSpeedLocalDaytimeMin WindSpeedLocalEveningAvg WindSpeedLocalEveningMax WindSpeedLocalEveningMin WindSpeedLocalMorningAvg WindSpeedLocalMorningMax WindSpeedLocalMorningMin WindSpeedLocalNighttimeAvg WindSpeedLocalNighttimeMax WindSpeedLocalNighttimeMin WindSpeedLocalOvernightAvg WindSpeedLocalOvernightMax WindSpeedLocalOvernightMin ID_ESTACION
0 20150629 285.9 285.9 285.9 286.0 287.0 285.4 285.9 285.9 285.9 286.1 287.0 285.4 NaN NaN NaN 285.5 287.0 283.9 284.9 286.0 283.9 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 307.6 307.6 307.6 304.1 307.6 298.6 307.6 307.6 307.6 302.1 307.4 295.4 NaN NaN NaN 296.9 307.4 290.1 291.7 294.4 290.1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 27.0 27.0 27.0 34.3 47.1 26.3 27.0 27.0 27.0 39.3 57.0 26.3 NaN NaN NaN 52.0 69.7 26.3 64.7 69.7 54.8 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 307.6 307.6 307.6 304.1 307.6 298.6 307.6 307.6 307.6 302.0 307.4 295.4 NaN NaN NaN 296.9 307.4 290.1 291.7 294.4 290.1 2.0 2 2 0.5 2 0 2.0 2 2 0.2 1 0 NaN NaN NaN 0.1 1 0 0 0 0 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 NaN NaN NaN 16093.0 16093 16093 16093.0 16093 16093 5.2 5.2 5.2 4.0 5.2 2.3 5.2 5.2 5.2 3.5 5.0 2.0 NaN NaN NaN 2.6 5.0 1.1 1.7 2.1 1.1 13
1 20150630 283.0 283.6 282.5 284.3 286.5 282.5 283.2 283.9 282.5 285.6 286.0 285.0 283.5 283.9 283.0 285.4 286.0 283.9 285.1 286.0 283.9 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 308.0 309.0 305.8 299.2 309.0 289.6 302.0 309.0 289.6 301.3 307.4 295.7 296.0 303.6 289.6 296.9 307.4 290.8 292.5 294.6 290.8 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.0 7.1 4.6 NaN NaN NaN 6.0 7.1 4.6 NaN NaN NaN 6.0 7.1 4.6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21.9 24.6 20.1 43.6 69.7 20.1 35.0 66.3 20.1 39.1 54.1 25.7 48.2 66.3 28.8 50.8 65.1 25.7 62.6 65.1 57.9 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 308.0 309.0 305.8 299.2 309.0 289.6 302.0 309.0 289.6 301.3 307.4 295.7 296.0 303.6 289.6 296.9 307.4 290.8 292.5 294.6 290.8 6.7 9 2 2.5 9 0 4.8 9 0 0.2 1 0 3.0 8.0 0.0 0.1 1 0 0 0 0 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093.0 16093.0 16093.0 16093 16093 16093.0 16093 16093 5.5 5.9 4.6 3.3 5.9 1.1 3.9 5.9 1.4 3.8 5.2 2.1 2.2 3.8 1.4 2.7 5.2 1.4 1.5 1.9 1.4 13
2 20150701 286.1 286.5 285.5 285.8 288.0 283.8 285.4 286.5 283.8 287.8 288.4 286.8 284.8 285.5 283.8 287.5 288.6 286.4 287.2 288.6 286.4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 305.1 305.9 303.8 298.3 305.9 290.8 300.7 305.9 290.9 299.4 303.4 294.9 296.3 302.4 290.9 296.6 303.4 293.3 293.8 294.3 293.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.4 32.6 28.9 48.1 66.1 28.9 40.7 66.1 28.9 50.6 66.5 36.5 50.0 66.1 31.6 58.1 71.5 36.5 65.7 71.5 63.8 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 305.1 305.9 303.8 298.3 305.9 290.8 300.7 305.9 290.9 299.2 303.4 294.9 296.3 302.4 290.9 296.5 303.4 293.3 293.8 294.3 293.3 7.3 10 3 2.7 10 0 5.3 10 0 0.2 1 0 3.3 8.0 0.0 0.1 1 0 0 0 0 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093.0 16093.0 16093.0 16093 16093 16093.0 16093 16093 6.1 6.5 5.3 3.6 6.5 1.2 4.4 6.5 1.2 4.1 5.7 2.2 2.7 4.3 1.2 3.0 5.7 1.4 1.9 2.8 1.4 13
3 20150702 288.9 289.4 287.9 288.3 291.8 286.1 287.7 289.4 286.1 291.0 291.8 289.6 286.6 287.3 286.1 291.2 291.9 289.6 291.4 291.9 290.5 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 302.0 303.8 299.8 297.6 303.8 293.2 299.0 303.8 293.2 298.6 302.6 294.8 295.9 299.5 293.2 296.0 302.6 292.9 293.5 293.9 292.9 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 6.3 7.3 5.7 NaN NaN NaN 6.1 7.3 5.5 NaN NaN NaN 6.0 7.3 5.3 5.3 5.3 5.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 47.1 50.7 43.5 58.5 77.6 43.5 52.0 66.8 43.5 66.7 81.2 48.1 56.8 66.8 47.3 77.2 93.6 48.1 87.7 93.6 80.8 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 301.2 302.9 298.9 297.2 302.9 293.2 298.5 302.9 293.2 297.9 301.7 294.8 295.8 298.7 293.2 295.7 301.7 292.9 293.5 293.9 292.9 6.5 8 3 2.3 8 0 4.6 8 0 0.2 1 0 2.7 7.0 0.0 0.1 1 0 0 0 0 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093.0 16093.0 16093.0 16093 16093 16093.0 16093 16093 4.0 4.8 2.8 3.0 5.4 1.2 2.8 4.8 1.2 4.5 5.4 3.5 1.7 2.3 1.2 3.6 5.4 2.4 2.7 3.4 2.4 13
4 20150703 289.1 290.4 288.5 290.5 293.0 287.8 290.6 293.0 288.5 289.2 290.9 287.8 292.1 293.0 290.9 288.9 290.9 287.4 288.6 289.6 287.4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 304.2 305.1 302.6 298.2 305.1 292.6 300.4 305.1 292.6 298.6 303.2 295.0 296.5 301.3 292.6 295.6 303.2 290.9 292.5 293.9 290.9 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 5.4 5.5 5.3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 42.5 51.4 39.1 67.5 98.7 39.1 61.4 98.7 39.1 58.3 73.9 40.0 80.3 98.7 57.9 68.2 80.8 40.0 78.1 80.8 75.8 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 303.4 304.2 301.4 297.8 304.2 292.6 299.7 304.2 292.6 298.4 302.8 295.0 296.0 299.8 292.6 295.5 302.8 290.9 292.5 293.9 290.9 7.3 10 3 2.6 10 0 5.2 10 0 0.2 1 0 3.0 8.0 0.0 0.1 1 0 0 0 0 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093 16093 16093.0 16093.0 16093.0 16093.0 16093 16093 16093.0 16093 16093 5.9 6.5 5.3 4.2 6.5 2.3 4.7 6.5 2.3 4.4 6.0 3.1 3.5 5.0 2.3 3.1 6.0 1.5 1.9 2.2 1.5 13
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
51175 20220626 284.0 285.0 283.0 285.0 288.0 283.0 285.0 286.0 283.0 288.0 288.0 287.0 286.0 286.0 285.0 287.0 288.0 286.0 287.0 288.0 286.0 0.7 0.8 0.6 0.3 0.8 0.0 0.5 0.8 0.0 0.2 0.5 0.0 0.3 0.6 0.0 0.1 0.5 0.0 0.0 0.0 0.0 303.0 304.0 301.0 296.0 304.0 287.0 298.0 304.0 288.0 297.0 301.0 293.0 294.0 300.0 288.0 294.0 301.0 289.0 290.0 292.0 289.0 895.0 999.0 679.0 357.0 999.0 0.0 637.0 999.0 1.2 154.0 498.0 0.0 379.0 806.0 1.2 77.3 498.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 100855.0 100990.0 100780.0 100980.0 101120.0 100780.0 100963.0 101120.0 100780.0 100973.0 101150.0 100800.0 101072.0 101120.0 101040.0 101058.0 101210.0 100800.0 101143.0 101210.0 101120.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 30.4 35.3 26.4 55.3 81.4 26.4 45.4 81.4 26.4 56.9 67.7 42.7 60.4 81.4 40.4 69.5 85.6 42.7 82.2 85.6 74.8 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 303.0 304.0 301.0 296.0 304.0 287.0 298.0 304.0 288.0 297.0 301.0 293.0 294.0 300.0 288.0 294.0 301.0 289.0 290.0 292.0 289.0 6.7 9 2 2.5 9 0 4.8 9 0 0.2 1 0 3.0 8.0 0.0 0.1 1 0 0 0 0 13017.0 13790 12575 13359.0 14454 12514 13000.0 14454 12514 13539.0 13950 12722 12983.0 14454.0 12514.0 13441.0 13950 12722 13344.0 13926 12740 3.7 4.6 3.2 2.5 5.2 1.0 2.9 4.6 1.2 3.0 5.2 1.2 2.1 2.6 1.2 2.1 5.2 0.6 1.1 1.6 0.6 8
51176 20220627 289.0 289.0 288.0 288.0 290.0 286.0 289.0 290.0 288.0 290.0 290.0 288.0 289.0 290.0 288.0 289.0 290.0 287.0 288.0 289.0 287.0 0.5 0.5 0.5 0.2 0.5 0.0 0.3 0.5 0.0 0.1 0.4 0.0 0.2 0.4 0.0 0.1 0.4 0.0 0.0 0.0 0.0 297.0 298.0 296.0 294.0 298.0 289.0 295.0 298.0 289.0 294.0 297.0 291.0 293.0 295.0 289.0 291.0 297.0 287.0 289.0 290.0 287.0 772.0 846.0 636.0 304.0 846.0 0.0 536.0 846.0 1.0 145.0 473.0 0.0 301.0 657.0 1.0 72.5 473.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 101473.0 101510.0 101460.0 101405.0 101930.0 101120.0 101418.0 101510.0 101280.0 101778.0 101990.0 101540.0 101363.0 101440.0 101280.0 101848.0 101990.0 101540.0 101917.0 101950.0 101900.0 0.0 0.1 0.0 0.0 0.6 0.0 0.1 0.6 0.0 0.0 0.0 0.0 0.1 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 58.2 63.1 55.9 72.8 90.9 55.9 67.8 90.9 55.9 77.7 92.7 63.1 77.4 90.9 64.4 86.6 100.0 63.1 95.4 100.0 91.3 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 297.0 298.0 296.0 294.0 298.0 289.0 295.0 298.0 289.0 294.0 297.0 291.0 293.0 295.0 289.0 291.0 297.0 287.0 289.0 290.0 287.0 6.7 9 2 2.4 9 0 4.8 9 0 0.2 1 0 2.8 8.0 0.0 0.1 1 0 0 0 0 12564.0 12846 11685 12205.0 13950 8046 11998.0 13857 9605 10498.0 12716 8046 11431.0 13857.0 9605.0 9272.0 12716 8046 8046.0 8046 8046 3.9 4.9 3.3 3.0 5.0 0.6 3.5 4.9 2.2 4.3 5.0 3.3 3.2 3.9 2.2 3.1 5.0 1.4 1.8 2.8 1.4 8
51177 20220628 283.0 285.0 282.0 286.0 290.0 282.0 284.0 287.0 282.0 287.0 288.0 286.0 286.0 287.0 284.0 287.0 288.0 285.0 286.0 288.0 285.0 0.7 0.7 0.6 0.3 0.7 0.0 0.4 0.7 0.0 0.2 0.5 0.0 0.2 0.5 0.0 0.1 0.5 0.0 0.0 0.0 0.0 302.0 303.0 301.0 295.0 303.0 287.0 298.0 303.0 287.0 297.0 301.0 293.0 294.0 299.0 287.0 293.0 301.0 288.0 290.0 292.0 288.0 900.0 1000.0 687.0 356.0 1000.0 0.0 633.0 1000.0 0.9 157.0 507.0 0.0 365.0 815.0 0.9 78.5 507.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 101630.0 101790.0 101510.0 101785.0 101990.0 101510.0 101767.0 101950.0 101510.0 101655.0 101780.0 101520.0 101903.0 101950.0 101850.0 101651.0 101780.0 101520.0 101647.0 101750.0 101570.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 29.4 33.4 27.1 62.4 100.0 27.1 47.5 98.0 27.1 56.2 74.0 38.1 65.5 98.0 40.0 68.3 81.8 38.1 80.5 81.8 78.6 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 302.0 303.0 301.0 295.0 303.0 287.0 298.0 303.0 287.0 297.0 301.0 293.0 294.0 299.0 287.0 293.0 301.0 288.0 290.0 292.0 288.0 6.7 9 2 2.5 9 0 4.8 9 0 0.2 1 0 3.0 8.0 0.0 0.1 1 0 0 0 0 12907.0 12983 12681 11275.0 14599 8046 12089.0 12983 8046 13975.0 14639 13058 11271.0 12881.0 8046.0 13913.0 14639 13058 13850.0 14314 13497 3.2 4.3 2.0 2.7 5.0 1.2 2.5 4.3 1.2 3.6 5.0 1.9 1.8 2.5 1.2 2.4 5.0 1.0 1.3 1.7 1.0 8
51178 20220629 283.0 285.0 282.0 285.0 288.0 282.0 284.0 286.0 282.0 286.0 287.0 285.0 285.0 286.0 283.0 286.0 287.0 285.0 286.0 287.0 286.0 0.7 0.8 0.6 0.3 0.8 0.0 0.5 0.8 0.0 0.2 0.5 0.0 0.3 0.6 0.0 0.1 0.5 0.0 0.0 0.0 0.0 304.0 304.0 303.0 297.0 304.0 288.0 300.0 304.0 289.0 297.0 302.0 293.0 296.0 302.0 289.0 294.0 302.0 289.0 290.0 292.0 289.0 878.0 982.0 638.0 354.0 982.0 0.0 629.0 982.0 1.0 118.0 465.0 0.0 380.0 806.0 1.0 53.5 465.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 101237.0 101400.0 101100.0 101421.0 101780.0 101060.0 101388.0 101590.0 101100.0 101183.0 101320.0 101060.0 101540.0 101590.0 101440.0 101206.0 101320.0 101060.0 101228.0 101320.0 101150.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 28.6 33.6 25.4 53.1 81.8 25.4 39.9 79.8 25.4 51.0 67.3 36.2 51.2 79.8 30.1 64.0 83.1 36.2 77.1 83.1 72.6 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 304.0 304.0 303.0 297.0 304.0 288.0 300.0 304.0 289.0 297.0 302.0 293.0 296.0 302.0 289.0 294.0 302.0 289.0 290.0 292.0 289.0 6.7 9 2 2.5 9 0 4.8 9 0 0.2 1 0 3.0 8.0 0.0 0.1 1 0 0 0 0 12906.0 12956 12822 13425.0 14639 12221 13014.0 14397 12221 13886.0 15023 12858 13121.0 14397.0 12221.0 14392.0 15184 12858 14897.0 15184 14084 4.9 5.9 3.3 3.0 5.9 1.0 3.6 5.9 1.3 3.9 5.4 2.5 2.2 3.1 1.3 2.7 5.4 1.3 1.6 1.9 1.3 8
51179 20220630 283.0 286.0 281.0 285.0 288.0 281.0 283.0 286.0 281.0 287.0 288.0 286.0 284.0 286.0 281.0 289.0 291.0 286.0 290.0 291.0 290.0 0.6 0.7 0.5 0.3 0.7 0.0 0.4 0.7 0.0 0.2 0.5 0.0 0.3 0.5 0.0 0.1 0.5 0.0 0.0 0.0 0.0 304.0 305.0 303.0 297.0 305.0 289.0 300.0 305.0 289.0 297.0 302.0 293.0 296.0 302.0 289.0 294.0 302.0 291.0 292.0 292.0 291.0 878.0 975.0 672.0 353.0 975.0 0.0 629.0 975.0 0.9 153.0 495.0 0.0 380.0 810.0 0.9 76.7 495.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 101007.0 101090.0 100940.0 101167.0 101440.0 100940.0 101108.0 101250.0 100940.0 101253.0 101510.0 100980.0 101210.0 101250.0 101140.0 101403.0 101590.0 100980.0 101553.0 101590.0 101530.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27.2 33.9 23.6 51.7 83.1 23.6 38.1 83.1 23.6 54.8 75.5 38.8 49.0 83.1 26.5 73.1 95.1 38.8 91.3 95.1 88.6 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 304.0 305.0 303.0 297.0 305.0 289.0 300.0 305.0 289.0 297.0 302.0 293.0 296.0 302.0 289.0 294.0 302.0 291.0 292.0 292.0 291.0 6.7 9 2 2.5 9 0 4.8 9 0 0.2 1 0 3.0 8.0 0.0 0.1 1 0 0 0 0 12812.0 13113 12563 13636.0 15184 12315 13031.0 14525 12315 13277.0 13846 12720 13250.0 14525.0 12315.0 10542.0 13846 6482 7808.0 9514 6482 2.7 3.9 1.1 2.2 4.4 0.8 2.0 3.9 0.8 3.1 4.4 2.2 1.2 1.6 0.8 3.4 4.4 2.2 3.6 3.9 2.9 8

51180 rows × 275 columns

Now we are going to change the format of the variable date into dates that look better and create 3 columns for the Year, Month and Day.

In [4]:
ETO["date"] = [i[:4] + "-" + i[4:6] + "-" + i[6:] for i in map(str,ETO.date)]
ETO[["Year", "Month", "Day"]] = ETO.date.str.split("-", expand = True)
ETO.loc[ETO.Month.isin([i for i in ETO.Month.unique() if int(i) <= 9]), "Month"] = [i.replace("0", "") for i in ETO.Month if int(i) < 10]
ETO.loc[ETO.Day.isin([i for i in ETO.Day.unique() if int(i) <= 9]), "Day"] = [i.replace("0", "") for i in ETO.Day if int(i) < 10]

Let's keep the columns that have the daily data and change the index.

In [5]:
ETO = (ETO.set_index(["ID_ESTACION", "Day", "Month", "Year"])
          .filter(regex='.*LocalDay(?!time).*')).reset_index()

ETO
Out[5]:
ID_ESTACION Day Month Year DewpointLocalDayAvg DewpointLocalDayMax DewpointLocalDayMin EvapotranspirationLocalDayAvg EvapotranspirationLocalDayMax EvapotranspirationLocalDayMin FeelsLikeLocalDayAvg FeelsLikeLocalDayMax FeelsLikeLocalDayMin GlobalHorizontalIrradianceLocalDayAvg GlobalHorizontalIrradianceLocalDayMax GlobalHorizontalIrradianceLocalDayMin GustLocalDayAvg GustLocalDayMax GustLocalDayMin MSLPLocalDayAvg MSLPLocalDayMax MSLPLocalDayMin PrecipAmountLocalDayAvg PrecipAmountLocalDayMax PrecipAmountLocalDayMin RelativeHumidityLocalDayAvg RelativeHumidityLocalDayMax RelativeHumidityLocalDayMin SnowAmountLocalDayAvg SnowAmountLocalDayMax SnowAmountLocalDayMin TemperatureLocalDayAvg TemperatureLocalDayMax TemperatureLocalDayMin UVIndexLocalDayAvg UVIndexLocalDayMax UVIndexLocalDayMin VisibilityLocalDayAvg VisibilityLocalDayMax VisibilityLocalDayMin WindSpeedLocalDayAvg WindSpeedLocalDayMax WindSpeedLocalDayMin
0 13 29 6 2015 286.0 287.0 285.4 NaN NaN NaN 304.1 307.6 298.6 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 34.3 47.1 26.3 0.0 0.0 0 304.1 307.6 298.6 0.5 2 0 16093.0 16093 16093 4.0 5.2 2.3
1 13 30 6 2015 284.3 286.5 282.5 NaN NaN NaN 299.2 309.0 289.6 NaN NaN NaN 6.0 7.1 4.6 NaN NaN NaN 0.0 0.0 0.0 43.6 69.7 20.1 0.0 0.0 0 299.2 309.0 289.6 2.5 9 0 16093.0 16093 16093 3.3 5.9 1.1
2 13 1 7 2015 285.8 288.0 283.8 NaN NaN NaN 298.3 305.9 290.8 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 48.1 66.1 28.9 0.0 0.0 0 298.3 305.9 290.8 2.7 10 0 16093.0 16093 16093 3.6 6.5 1.2
3 13 2 7 2015 288.3 291.8 286.1 NaN NaN NaN 297.6 303.8 293.2 NaN NaN NaN 6.3 7.3 5.7 NaN NaN NaN 0.0 0.0 0.0 58.5 77.6 43.5 0.0 0.0 0 297.2 302.9 293.2 2.3 8 0 16093.0 16093 16093 3.0 5.4 1.2
4 13 3 7 2015 290.5 293.0 287.8 NaN NaN NaN 298.2 305.1 292.6 NaN NaN NaN 5.4 5.5 5.3 NaN NaN NaN 0.0 0.0 0.0 67.5 98.7 39.1 0.0 0.0 0 297.8 304.2 292.6 2.6 10 0 16093.0 16093 16093 4.2 6.5 2.3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
51175 8 26 6 2022 285.0 288.0 283.0 0.3 0.8 0.0 296.0 304.0 287.0 357.0 999.0 0.0 NaN NaN NaN 100980.0 101120.0 100780.0 0.0 0.0 0.0 55.3 81.4 26.4 0.0 0.0 0 296.0 304.0 287.0 2.5 9 0 13359.0 14454 12514 2.5 5.2 1.0
51176 8 27 6 2022 288.0 290.0 286.0 0.2 0.5 0.0 294.0 298.0 289.0 304.0 846.0 0.0 NaN NaN NaN 101405.0 101930.0 101120.0 0.0 0.6 0.0 72.8 90.9 55.9 0.0 0.0 0 294.0 298.0 289.0 2.4 9 0 12205.0 13950 8046 3.0 5.0 0.6
51177 8 28 6 2022 286.0 290.0 282.0 0.3 0.7 0.0 295.0 303.0 287.0 356.0 1000.0 0.0 NaN NaN NaN 101785.0 101990.0 101510.0 0.0 0.0 0.0 62.4 100.0 27.1 0.0 0.0 0 295.0 303.0 287.0 2.5 9 0 11275.0 14599 8046 2.7 5.0 1.2
51178 8 29 6 2022 285.0 288.0 282.0 0.3 0.8 0.0 297.0 304.0 288.0 354.0 982.0 0.0 NaN NaN NaN 101421.0 101780.0 101060.0 0.0 0.0 0.0 53.1 81.8 25.4 0.0 0.0 0 297.0 304.0 288.0 2.5 9 0 13425.0 14639 12221 3.0 5.9 1.0
51179 8 30 6 2022 285.0 288.0 281.0 0.3 0.7 0.0 297.0 305.0 289.0 353.0 975.0 0.0 NaN NaN NaN 101167.0 101440.0 100940.0 0.0 0.0 0.0 51.7 83.1 23.6 0.0 0.0 0 297.0 305.0 289.0 2.5 9 0 13636.0 15184 12315 2.2 4.4 0.8

51180 rows × 43 columns

And now let us analyse the existence of missing data in our variables.

In [6]:
ETO.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 51180 entries, 0 to 51179
Data columns (total 43 columns):
 #   Column                                 Non-Null Count  Dtype  
---  ------                                 --------------  -----  
 0   ID_ESTACION                            51180 non-null  int64  
 1   Day                                    51180 non-null  object 
 2   Month                                  51180 non-null  object 
 3   Year                                   51180 non-null  object 
 4   DewpointLocalDayAvg                    51180 non-null  float64
 5   DewpointLocalDayMax                    51180 non-null  float64
 6   DewpointLocalDayMin                    51180 non-null  float64
 7   EvapotranspirationLocalDayAvg          16600 non-null  float64
 8   EvapotranspirationLocalDayMax          16600 non-null  float64
 9   EvapotranspirationLocalDayMin          16600 non-null  float64
 10  FeelsLikeLocalDayAvg                   51180 non-null  float64
 11  FeelsLikeLocalDayMax                   51180 non-null  float64
 12  FeelsLikeLocalDayMin                   51180 non-null  float64
 13  GlobalHorizontalIrradianceLocalDayAvg  16600 non-null  float64
 14  GlobalHorizontalIrradianceLocalDayMax  16600 non-null  float64
 15  GlobalHorizontalIrradianceLocalDayMin  16600 non-null  float64
 16  GustLocalDayAvg                        14415 non-null  float64
 17  GustLocalDayMax                        14415 non-null  float64
 18  GustLocalDayMin                        14415 non-null  float64
 19  MSLPLocalDayAvg                        36200 non-null  float64
 20  MSLPLocalDayMax                        36200 non-null  float64
 21  MSLPLocalDayMin                        36200 non-null  float64
 22  PrecipAmountLocalDayAvg                51180 non-null  float64
 23  PrecipAmountLocalDayMax                51180 non-null  float64
 24  PrecipAmountLocalDayMin                51180 non-null  float64
 25  RelativeHumidityLocalDayAvg            51180 non-null  float64
 26  RelativeHumidityLocalDayMax            51180 non-null  float64
 27  RelativeHumidityLocalDayMin            51180 non-null  float64
 28  SnowAmountLocalDayAvg                  51180 non-null  float64
 29  SnowAmountLocalDayMax                  51180 non-null  float64
 30  SnowAmountLocalDayMin                  51180 non-null  int64  
 31  TemperatureLocalDayAvg                 51180 non-null  float64
 32  TemperatureLocalDayMax                 51180 non-null  float64
 33  TemperatureLocalDayMin                 51180 non-null  float64
 34  UVIndexLocalDayAvg                     51180 non-null  float64
 35  UVIndexLocalDayMax                     51180 non-null  int64  
 36  UVIndexLocalDayMin                     51180 non-null  int64  
 37  VisibilityLocalDayAvg                  51180 non-null  float64
 38  VisibilityLocalDayMax                  51180 non-null  int64  
 39  VisibilityLocalDayMin                  51180 non-null  int64  
 40  WindSpeedLocalDayAvg                   51180 non-null  float64
 41  WindSpeedLocalDayMax                   51180 non-null  float64
 42  WindSpeedLocalDayMin                   51180 non-null  float64
dtypes: float64(34), int64(6), object(3)
memory usage: 16.8+ MB
In [7]:
ETO.describe()
Out[7]:
ID_ESTACION DewpointLocalDayAvg DewpointLocalDayMax DewpointLocalDayMin EvapotranspirationLocalDayAvg EvapotranspirationLocalDayMax EvapotranspirationLocalDayMin FeelsLikeLocalDayAvg FeelsLikeLocalDayMax FeelsLikeLocalDayMin GlobalHorizontalIrradianceLocalDayAvg GlobalHorizontalIrradianceLocalDayMax GlobalHorizontalIrradianceLocalDayMin GustLocalDayAvg GustLocalDayMax GustLocalDayMin MSLPLocalDayAvg MSLPLocalDayMax MSLPLocalDayMin PrecipAmountLocalDayAvg PrecipAmountLocalDayMax PrecipAmountLocalDayMin RelativeHumidityLocalDayAvg RelativeHumidityLocalDayMax RelativeHumidityLocalDayMin SnowAmountLocalDayAvg SnowAmountLocalDayMax SnowAmountLocalDayMin TemperatureLocalDayAvg TemperatureLocalDayMax TemperatureLocalDayMin UVIndexLocalDayAvg UVIndexLocalDayMax UVIndexLocalDayMin VisibilityLocalDayAvg VisibilityLocalDayMax VisibilityLocalDayMin WindSpeedLocalDayAvg WindSpeedLocalDayMax WindSpeedLocalDayMin
count 51180.000000 51180.000000 51180.000000 51180.000000 16600.000000 16600.000000 16600.000000 51180.000000 51180.000000 51180.000000 16600.000000 16600.000000 16600.0 14415.000000 14415.000000 14415.000000 36200.000000 36200.000000 36200.000000 51180.000000 51180.000000 51180.000000 51180.000000 51180.000000 51180.000000 51180.000000 51180.000000 51180.0 51180.000000 51180.000000 51180.000000 51180.000000 51180.000000 51180.0 51180.000000 51180.000000 51180.000000 51180.000000 51180.000000 51180.000000
mean 9.500000 281.334281 283.782683 278.840741 0.148729 0.435831 0.000476 287.853298 294.109048 282.337888 215.286355 704.958729 0.0 8.445522 9.809067 7.183580 101775.754633 102012.040884 101554.076519 0.065903 0.495397 0.001458 66.909105 87.593103 43.377737 0.000007 0.000033 0.0 288.529439 294.480045 283.347306 1.358054 5.634682 0.0 12873.402044 14737.164713 10321.934857 2.915457 4.817522 1.323185
std 5.766338 5.779998 5.698248 6.048484 0.091599 0.206679 0.006882 7.672332 8.746639 6.937972 95.095702 233.239807 0.0 1.802888 2.941010 1.242222 624.716678 591.425803 659.088147 0.301532 2.070037 0.046220 14.906146 12.223877 17.524015 0.000202 0.000896 0.0 6.863879 7.965405 6.097857 0.803475 2.850148 0.0 2347.294792 1509.757098 3941.034309 1.507220 2.033228 1.086706
min 0.000000 261.000000 264.000000 256.000000 0.000000 0.100000 0.000000 266.000000 269.000000 261.900000 16.600000 74.100000 0.0 0.000000 0.000000 0.000000 99276.000000 99660.000000 98860.000000 0.000000 0.000000 0.000000 22.300000 37.300000 6.500000 0.000000 0.000000 0.0 271.000000 273.000000 265.300000 0.100000 1.000000 0.0 453.000000 804.000000 96.000000 0.500000 1.000000 0.000000
25% 4.750000 277.000000 280.000000 274.700000 0.100000 0.300000 0.000000 281.400000 287.000000 277.000000 130.000000 522.000000 0.0 7.500000 7.700000 7.200000 101406.150000 101640.000000 101180.000000 0.000000 0.000000 0.000000 56.400000 79.700000 30.500000 0.000000 0.000000 0.0 283.000000 288.000000 278.500000 0.500000 3.000000 0.0 11598.075000 14031.000000 8046.000000 1.900000 3.400000 0.600000
50% 9.500000 281.200000 283.700000 279.000000 0.100000 0.400000 0.000000 287.100000 294.000000 282.000000 214.550000 733.700000 0.0 8.300000 9.200000 7.400000 101738.000000 101960.000000 101540.000000 0.000000 0.000000 0.000000 66.500000 90.900000 40.500000 0.000000 0.000000 0.0 287.900000 294.000000 283.000000 1.300000 6.000000 0.0 13236.000000 14895.000000 10982.000000 2.500000 4.400000 1.000000
75% 14.250000 286.000000 288.300000 283.300000 0.200000 0.600000 0.000000 294.700000 301.400000 288.300000 307.000000 924.225000 0.0 9.400000 11.500000 7.700000 102157.000000 102370.000000 101960.000000 0.000000 0.000000 0.000000 77.500000 98.900000 52.900000 0.000000 0.000000 0.0 294.600000 301.000000 288.500000 2.200000 9.000000 0.0 14189.000000 16093.000000 13012.000000 3.400000 5.700000 1.600000
max 19.000000 295.000000 296.400000 294.000000 0.500000 1.100000 0.100000 306.500000 316.300000 301.100000 393.000000 1026.000000 0.0 19.400000 26.100000 15.400000 103557.000000 103700.000000 103420.000000 8.100000 60.300000 2.500000 100.000000 100.000000 100.000000 0.010000 0.050000 0.0 306.200000 316.300000 300.200000 3.000000 10.000000 0.0 16093.000000 16093.000000 16093.000000 12.900000 16.300000 10.800000

EDA¶

In [8]:
columns_with_missing_data_ETO = ETO.isnull().sum()[ETO.isnull().sum() > 0].index
ETO.groupby('Year')[columns_with_missing_data_ETO].apply(lambda x: x.isnull().sum())
Out[8]:
EvapotranspirationLocalDayAvg EvapotranspirationLocalDayMax EvapotranspirationLocalDayMin GlobalHorizontalIrradianceLocalDayAvg GlobalHorizontalIrradianceLocalDayMax GlobalHorizontalIrradianceLocalDayMin GustLocalDayAvg GustLocalDayMax GustLocalDayMin MSLPLocalDayAvg MSLPLocalDayMax MSLPLocalDayMin
Year
2015 3720 3720 3720 3720 3720 3720 2540 2540 2540 3720 3720 3720
2016 7320 7320 7320 7320 7320 7320 5095 5095 5095 7320 7320 7320
2017 7300 7300 7300 7300 7300 7300 5777 5777 5777 3940 3940 3940
2018 7300 7300 7300 7300 7300 7300 5720 5720 5720 0 0 0
2019 7300 7300 7300 7300 7300 7300 4787 4787 4787 0 0 0
2020 1260 1260 1260 1260 1260 1260 5101 5101 5101 0 0 0
2021 380 380 380 380 380 380 5239 5239 5239 0 0 0
2022 0 0 0 0 0 0 2506 2506 2506 0 0 0
In [9]:
sns.heatmap(ETO[columns_with_missing_data_ETO].isnull(), cbar=False);
In [10]:
fig, ax = plt.subplots(figsize=(28, 60))
g = sns.barplot(y=ETO[columns_with_missing_data_ETO].columns, x=ETO[columns_with_missing_data_ETO].isnull().sum(), orient = 'h', palette = 'Blues')

We can see that we have missing data in "EvapotranspirationLocalDayAvg", "EvapotranspirationLocalDayMax", "EvapotranspirationLocalDayMin", "GlobalHorizontalIrradianceLocalDayAvg", "GlobalHorizontalIrradianceLocalDayMax", "GlobalHorizontalIrradianceLocalDayMin", "GustLocalDayAvg", "GustLocalDayMax", "GustLocalDayMin", "MSLPLocalDayAvg", "MSLPLocalDayMax", "MSLPLocalDayMin".

In [11]:
sns.set_theme(style="white")

columns = [col for col in ETO.columns if ('Avg' in col[-3:])]

g = sns.PairGrid(ETO[columns], diag_sharey=False)
g.map_upper(sns.scatterplot, s=15)
g.map_lower(sns.scatterplot, s=15)
g.map_diag(sns.kdeplot, lw=2)
plt.show()
In [12]:
correlation_matrix = ETO[columns].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='Blues', annot_kws={"size": 8})
plt.show()
In [13]:
variable_columns = {
    'DewpointLocalDay': ['DewpointLocalDayMax', 'DewpointLocalDayAvg', 'DewpointLocalDayMin'],
    'EvapotranspirationLocalDay': ['EvapotranspirationLocalDayMax', 'EvapotranspirationLocalDayAvg', 'EvapotranspirationLocalDayMin'],
    'FeelsLikeLocalDay': ['FeelsLikeLocalDayMax', 'FeelsLikeLocalDayAvg', 'FeelsLikeLocalDayMin'],
    'GlobalHorizontalIrradianceLocalDay': ['GlobalHorizontalIrradianceLocalDayMax', 'GlobalHorizontalIrradianceLocalDayAvg', 'GlobalHorizontalIrradianceLocalDayMin'],
    'GustLocalDay': ['GustLocalDayMax', 'GustLocalDayAvg', 'GustLocalDayMin'],
    'MSLPLocalDay': ['MSLPLocalDayMax', 'MSLPLocalDayAvg', 'MSLPLocalDayMin'],
    'PrecipAmountLocalDay': ['PrecipAmountLocalDayMax', 'PrecipAmountLocalDayAvg', 'PrecipAmountLocalDayMin'],
    'RelativeHumidityLocalDay': ['RelativeHumidityLocalDayMax', 'RelativeHumidityLocalDayAvg', 'RelativeHumidityLocalDayMin'],
    'SnowAmountLocalDay': ['SnowAmountLocalDayMax', 'SnowAmountLocalDayAvg', 'SnowAmountLocalDayMin'],
    'TemperatureLocalDay': ['TemperatureLocalDayMax', 'TemperatureLocalDayAvg', 'TemperatureLocalDayMin'],
    'UVIndexLocalDay': ['UVIndexLocalDayMax', 'UVIndexLocalDayAvg', 'UVIndexLocalDayMin'],
    'VisibilityLocalDay': ['VisibilityLocalDayMax', 'VisibilityLocalDayAvg', 'VisibilityLocalDayMin'],
    'WindSpeedLocalDay': ['WindSpeedLocalDayMax', 'WindSpeedLocalDayAvg', 'WindSpeedLocalDayMin']
}

statistics = ["Max", "Avg", "Min"]
fig, axes = plt.subplots(nrows=len(variable_columns), ncols=len(statistics), figsize=(20, 35)
)


for i_row, (axes_row, variable) in enumerate(zip(axes, variable_columns.keys())):
    for ax, stat in zip(axes_row, statistics):
        eto_column = variable + stat
        
        try:
            data = ETO[eto_column]
        except KeyError:
            ax.axis("off")
            continue
            
        sns.histplot(data=data, kde=True, ax = ax)
        ax.set_title(eto_column)
        ax.set_xlabel("Value")
        ax.set_ylabel("Frequency")
        

plt.tight_layout()
plt.show()

DATA PREPROCESSING¶

In [14]:
ETO_MISS = ETO.groupby('Year')[columns_with_missing_data_ETO].apply(lambda x: x.isnull().sum())
for i, row in ETO_MISS.iterrows():
    ETO_MISS.loc[i,'TAMAÑO'] = len(ETO[ETO.Year == i])

ETO_MISS
Out[14]:
EvapotranspirationLocalDayAvg EvapotranspirationLocalDayMax EvapotranspirationLocalDayMin GlobalHorizontalIrradianceLocalDayAvg GlobalHorizontalIrradianceLocalDayMax GlobalHorizontalIrradianceLocalDayMin GustLocalDayAvg GustLocalDayMax GustLocalDayMin MSLPLocalDayAvg MSLPLocalDayMax MSLPLocalDayMin TAMAÑO
Year
2015 3720 3720 3720 3720 3720 3720 2540 2540 2540 3720 3720 3720 3720.0
2016 7320 7320 7320 7320 7320 7320 5095 5095 5095 7320 7320 7320 7320.0
2017 7300 7300 7300 7300 7300 7300 5777 5777 5777 3940 3940 3940 7300.0
2018 7300 7300 7300 7300 7300 7300 5720 5720 5720 0 0 0 7300.0
2019 7300 7300 7300 7300 7300 7300 4787 4787 4787 0 0 0 7300.0
2020 1260 1260 1260 1260 1260 1260 5101 5101 5101 0 0 0 7320.0
2021 380 380 380 380 380 380 5239 5239 5239 0 0 0 7300.0
2022 0 0 0 0 0 0 2506 2506 2506 0 0 0 3620.0
In [15]:
%%time
ETO['Fecha'] = pd.to_datetime(ETO[['Year','Month','Day']])
ETO['fechas_invertida'] = ETO['Fecha'][::-1].reset_index(drop=True)

grupos = ETO.groupby('ID_ESTACION')

dataframes = {}
for estacion, grupo in grupos:
    dataframes[estacion] = grupo.drop(grupo.index[0]).reset_index(level=0, drop=True)
    
dataframes_completos = {}

for estacion, df in dataframes.items():
    columns_aus = df.columns[df.isnull().any()].tolist()
    df_completo = df.copy(deep = True)
    
    for col in columns_aus:
        index_fechas_faltantes = df[df[col].isnull()].index.tolist()
        df_prophet_train = df[~df[col].isnull()].rename(columns={'fechas_invertida': 'ds', col: 'y'})[['ds', 'y']]
        predict = df[df[col].isnull()].rename(columns={'fechas_invertida': 'ds', col: 'y'})[['ds', 'y']]
        model = Prophet()
        model.fit(df_prophet_train)

        if predict.empty:
            pass
        else:
            forecast = model.predict(predict)

        df_completo[col] = df_completo[col].fillna(df_completo['fechas_invertida'].map(forecast.set_index('ds')['yhat']))
        fig = model.plot(forecast)
        
    dataframes_completos[estacion] = df_completo.drop(df_completo.index[0]).reset_index(level=0, drop=True)

ETO_PROPHET = pd.concat(dataframes_completos.values(), ignore_index=True)
ETO_PROPHET = ETO_PROPHET.drop(['fechas_invertida'], axis = 1)
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C:\Users\Usuario\anaconda3\Lib\site-packages\prophet\plot.py:67: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  fig = plt.figure(facecolor='w', figsize=figsize)
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CPU times: total: 36.3 s
Wall time: 1min 57s
In [16]:
ETO_PROPHET.groupby('Year')[columns_with_missing_data_ETO].apply(lambda x: x.isnull().sum())
Out[16]:
EvapotranspirationLocalDayAvg EvapotranspirationLocalDayMax EvapotranspirationLocalDayMin GlobalHorizontalIrradianceLocalDayAvg GlobalHorizontalIrradianceLocalDayMax GlobalHorizontalIrradianceLocalDayMin GustLocalDayAvg GustLocalDayMax GustLocalDayMin MSLPLocalDayAvg MSLPLocalDayMax MSLPLocalDayMin
Year
2015 0 0 0 0 0 0 0 0 0 0 0 0
2016 0 0 0 0 0 0 0 0 0 0 0 0
2017 0 0 0 0 0 0 0 0 0 0 0 0
2018 0 0 0 0 0 0 0 0 0 0 0 0
2019 0 0 0 0 0 0 0 0 0 0 0 0
2020 0 0 0 0 0 0 0 0 0 0 0 0
2021 0 0 0 0 0 0 0 0 0 0 0 0
2022 0 0 0 0 0 0 0 0 0 0 0 0

We can see that the data have been generated correctly and we are going to look at their distribution in case any transformation of the data is necessary.

In [17]:
ETO_FINAL = ETO_PROPHET.copy(deep = True)
for col in columns_with_missing_data_ETO:
    if (ETO[col].min() == 0) & (ETO_FINAL[col].min() < 0):
        ETO_FINAL[col] = ETO_FINAL[col].apply(lambda x: 0 if x < 0 else x)
        
    plt.figure(figsize=(8,4))
    sns.histplot(x=ETO[col], kde=True)
    sns.histplot(x=ETO_FINAL[col], kde=True)
    plt.show()
In [18]:
ETO_FINAL.to_csv('./Datos_Completos/DATOS_ETO_FINAL.csv', index=None)
ETO_FINAL
Out[18]:
ID_ESTACION Day Month Year DewpointLocalDayAvg DewpointLocalDayMax DewpointLocalDayMin EvapotranspirationLocalDayAvg EvapotranspirationLocalDayMax EvapotranspirationLocalDayMin FeelsLikeLocalDayAvg FeelsLikeLocalDayMax FeelsLikeLocalDayMin GlobalHorizontalIrradianceLocalDayAvg GlobalHorizontalIrradianceLocalDayMax GlobalHorizontalIrradianceLocalDayMin GustLocalDayAvg GustLocalDayMax GustLocalDayMin MSLPLocalDayAvg MSLPLocalDayMax MSLPLocalDayMin PrecipAmountLocalDayAvg PrecipAmountLocalDayMax PrecipAmountLocalDayMin RelativeHumidityLocalDayAvg RelativeHumidityLocalDayMax RelativeHumidityLocalDayMin SnowAmountLocalDayAvg SnowAmountLocalDayMax SnowAmountLocalDayMin TemperatureLocalDayAvg TemperatureLocalDayMax TemperatureLocalDayMin UVIndexLocalDayAvg UVIndexLocalDayMax UVIndexLocalDayMin VisibilityLocalDayAvg VisibilityLocalDayMax VisibilityLocalDayMin WindSpeedLocalDayAvg WindSpeedLocalDayMax WindSpeedLocalDayMin Fecha
0 0 1 7 2015 287.0 289.0 285.0 0.290284 0.764094 0.002100 298.0 304.0 291.0 378.722425 1087.637213 0.0 6.077841 6.816106 5.000474 101477.688944 101793.123089 101386.831260 0.0 0.0 0.0 52.7 68.7 34.7 0.0 0.0 0 298.0 304.0 291.0 2.8 10 0 16093.0 16093 16093 3.5 6.7 1.0 2015-07-01
1 0 2 7 2015 289.0 292.0 287.0 0.286786 0.776297 0.002372 297.0 303.0 293.0 383.922731 1098.553161 0.0 5.900000 5.900000 5.900000 101483.675822 101792.554493 101395.078487 0.0 0.0 0.0 63.5 77.0 50.2 0.0 0.0 0 297.0 302.0 293.0 2.2 9 0 16093.0 16093 16093 3.0 5.2 1.1 2015-07-02
2 0 3 7 2015 291.0 293.0 288.0 0.295327 0.771802 0.003496 298.0 303.0 294.0 379.653810 1083.890569 0.0 5.800000 5.800000 5.800000 101468.005454 101779.772492 101376.823678 0.0 0.0 0.0 70.7 96.2 45.1 0.0 0.0 0 297.0 302.0 294.0 2.6 10 0 16093.0 16093 16093 4.1 6.4 2.2 2015-07-03
3 0 4 7 2015 288.0 291.0 286.0 0.295483 0.781390 0.002889 298.0 304.0 291.0 383.891758 1092.450319 0.0 6.546586 7.605024 5.183511 101482.748414 101792.490192 101391.985130 0.0 0.0 0.0 59.5 83.4 33.1 0.0 0.0 0 298.0 304.0 291.0 2.5 10 0 16093.0 16093 16093 3.2 5.5 1.5 2015-07-04
4 0 5 7 2015 287.0 290.0 285.0 0.292981 0.780724 0.003949 300.0 310.0 290.0 384.936384 1105.077884 0.0 6.397455 7.586672 5.175269 101488.925750 101802.431451 101380.537140 0.0 0.0 0.0 51.8 84.6 24.6 0.0 0.0 0 300.0 310.0 290.0 2.7 10 0 16093.0 16093 16093 2.8 5.2 0.7 2015-07-05
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
51135 19 26 6 2022 287.0 289.0 284.0 0.300000 0.700000 0.000000 296.0 304.0 288.0 354.000000 987.000000 0.0 8.315059 9.095765 7.767655 100975.000000 101120.000000 100800.000000 0.0 0.0 0.0 58.8 79.4 32.4 0.0 0.0 0 296.0 304.0 288.0 2.5 9 0 13471.0 15059 12258 2.1 3.8 0.8 2022-06-26
51136 19 27 6 2022 290.0 291.0 288.0 0.200000 0.500000 0.000000 295.0 298.0 291.0 285.000000 836.000000 0.0 8.264504 8.873532 7.715101 101399.000000 101920.000000 101110.000000 0.0 0.0 0.0 74.9 92.7 57.4 0.0 0.0 0 295.0 298.0 291.0 2.3 9 0 11678.0 14030 8046 3.2 4.9 0.7 2022-06-27
51137 19 28 6 2022 287.0 291.0 283.0 0.300000 0.700000 0.000000 296.0 305.0 289.0 352.000000 993.000000 0.0 7.895230 8.124502 7.687772 101771.000000 101980.000000 101510.000000 0.0 0.0 0.0 62.3 100.0 27.8 0.0 0.0 0 296.0 305.0 289.0 2.4 9 0 11412.0 14706 8046 2.5 4.6 1.0 2022-06-28
51138 19 29 6 2022 286.0 289.0 282.0 0.300000 0.800000 0.000000 298.0 306.0 290.0 355.000000 984.000000 0.0 7.987187 8.313957 7.635171 101406.000000 101750.000000 101050.000000 0.0 0.0 0.0 51.6 81.8 24.7 0.0 0.0 0 298.0 306.0 290.0 2.4 9 0 13741.0 15063 12426 3.0 5.6 1.1 2022-06-29
51139 19 30 6 2022 287.0 289.0 284.0 0.300000 0.700000 0.000000 298.0 305.0 290.0 351.000000 971.000000 0.0 8.047332 8.431844 7.818192 101160.000000 101430.000000 100940.000000 0.0 0.0 0.0 53.1 76.7 28.0 0.0 0.0 0 298.0 305.0 290.0 2.4 9 0 13775.0 15414 12471 1.9 3.1 0.8 2022-06-30

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